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Why is data preprocessing important in Machine Learning?

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Preprocessing prepares and cleans data so the model can learn important patterns and avoid mistakes.

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What is Feature Engineering?

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Feature engineering transform raw data into meaningful features to improve model performance.

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How do you handle missing values?

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• 1- Remove rows/columns with too many missing values.
• 2- Replace missing values using mean, median, or mode (imputation).

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What is the purpose of feature scaling, and when should it be applied?

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Purpose: Feature scaling makes all data features have similar ranges, so no feature dominates others. This improves model performance.

When to Apply:

1. Use it with models like Logistic Regression, SVM, or KNN
2. When features have different units or ranges.